检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Reynaud, Hadrien Qiao, Mengyun Dombrowski, Mischa Day, Thomas Razavi, Reza Gomez, Alberto Leeson, Paul Kainz, Bernhard UKRI CDT in AI for Healthcare Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom Department of Brain Sciences Data Science Institute Imperial College London London United Kingdom Friedrich–Alexander University Erlangen Nürnberg Germany School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Congenital Cardiology Evelina London Children’s Healthcare Guy’s and St Thomas’ NHS Foundation Trust London United Kingdom Ultromics Ltd Oxford United Kingdom John Radcliffe Hospital Cardiovascular Clinical Research Facility Oxford United Kingdom

Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, causal modelling, and operator training become approachable through synthetic data. Especially, heavily operator-dependant modalities like Ultrasound imaging require robust frameworks for image and video generation. So far, video generation has only been possible by providing input data that is as rich as the output data, e.g., image sequence plus conditioning in → video out. However, clinical documentation is usually scarce and only single images are reported and stored, thus retrospective patient-specific analysis or the generation of rich training data becomes impossible with current approaches. In this paper, we extend elucidated diffusion models for video modelling to generate plausible video sequences from single images and arbitrary conditioning with clinical parameters. We explore this idea within the context of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We use the publicly available EchoNet-Dynamic dataset for all our experiments. Our image to sequence approach achieves an R2 score of 93%, which is 38 points higher than recently proposed sequence to sequence generation methods. Code and models will be available at: https://***/HReynaud/ EchoDiffusion. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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The Twitter Social Mobility Index: Measuring Social Distancing Practices from Geolocated Tweets

arXiv

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arXiv 2020年

作者： Xu, Paiheng Dredze, Mark Broniatowski, David A. Malone Center for Engineering in Healthcare Center for Language and Speech Processing Department of Computer Science Johns Hopkins University Department of Engineering Management and Systems Engineering Institute for Data Democracy and Politics The George Washington University

Social distancing is an important component of the response to the novel Coronavirus (COVID-19) pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads, and"flattens the curve" such that the medical system can better treat infected individuals. However, it remains unclear how the public will respond to these policies. This paper presents the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We use public geolocated Twitter data to measure how much a user travels in a given week. We find a large reduction in travel in the United States after the implementation of social distancing policies, with larger reductions in states that were early adopters and smaller changes in states without policies. Our findings are presented on http://*** and we will continue to update our analysis during the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Measurement of the Gamma-Ray Energy Spectrum beyond 100 TeV from the HESS J1843-033 Region

arXiv

引用

arXiv 2023年

作者： Amenomori, M. Asano, S. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gomi, A. Gou, Q.B. Guo, Y.Q. Guo, Y.Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawashima, T. Kawata, K. Kozai, M. Kurashige, D. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Liu, X.L. Lou, Y.-Q. Lu, H. Meng, X.R. Meng, Y. Munakata, K. Nagaya, K. Nakamura, Y. Nakazawa, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, L. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakakibara, Y. Sakata, M. Sako, T. Sako, T.K. Shao, J. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Wang, H. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, N.P. Yuan, A.F. Zhai, L.M. Zhang, C.P. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Department of Physics Hirosaki University Hirosaki036-8561 Japan Department of Physics Shinshu University Matsumoto390-8621 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China Tsinghua University Beijing100084 China Joint Research Center for Astrophysics Tsinghua University Beijing100084 China Department of Astronomy Tsinghua University Beijing100084 China College of Industrial Technology Nihon University Narashino275-8575 Japan National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo

HESS J1843-033 is a very-high-energy gamma-ray source whose origin remains unidentified. This work presents, for the first time, the energy spectrum of gamma rays beyond 100 TeV from the HESS J1843-033 region using the data recorded by the Tibet air shower array and its underground muon detector array. A gamma-ray source with an extension of 0◦. 34 ± 0◦. 12 is successfully detected above 25 TeV at (α, δ) = (281◦. 09 ± 0◦. 10, −3◦. 76 ± 0◦. 09) near HESS J1843-033 with a statistical significance of 6.2 σ, and the source is named TASG J1844-038. The position of TASG J1844-038 is consistent with those of HESS J1843-033, eHWC J1842-035, and LHAASO J1843-0338. The measured gamma-ray energy spectrum in 25 TeV −16(E/40 TeV)−3.26±0.30 TeV−1cm−2s−1, and the spectral fit to the combined spectra of HESS J1843-033, LHAASO J1843-0338, and TASG J1844-038 implies the existence of a cutoff at 49.5 ± 9.0 TeV. Associations of TASG J1844-038 with SNR G28.6-0.1 and PSR J1844-0346 are also discussed in detail for the first time. © 2023, CC BY.

关键词： Cosmology

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Gaseous Scissor-mediated Electrochemical Exfoliation of Halogenated MXenes and its Boosting in Wear-Resisting Tribovoltaic Devices

arXiv

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arXiv 2024年

作者： Fan, Qi Chen, Minghua Li, Longyi Li, Minghui Xiao, Chuanxiao Zhao, Tianci Pan, Long Liang, Ningning Huang, Qing Zhu, Laipan Naguib, Michael Liang, Kun Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing100049 China Beijing Key Laboratory of Micro-nano Energy and Sensor Center for High-Entropy Energy and Systems Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing101400 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Ningbo New Materials Testing and Evaluation Center Co. Ltd Ningbo315201 China School of Physics and Optoelectronic Engineering Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Metallic Materials of Jiangsu Province School of Materials Science and Engineering Southeast University Nanjing211189 China Qianwan Institute of CNITECH Ningbo315201 China Department of Physics and Engineering Physics Tulane University New OrleansLA70118 United States

Two-dimensional transition metal carbides (MXenes), especially their few-layered nanosheets, have triggered burgeoning research attentions owing to their superiorities including extraordinary conductivity, accessible active surface, and adjustable processability. Molten salts etching route further achieves their controllable surface chemistry. However, the method encounters challenges in achieving few-layer structures due to more complex delamination behaviors. Herein, we present an efficient strategy to fabricate Cl- or Br-terminated MXene nanoflakes with few-layers, achieved by electrochemical intercalation of Li ions and concomitant solvent molecules in the electrolyte solution, with gaseous scissors (propylene molecules) to break up interlayer forces. By controlling cut-off voltages, the optimal protocol results in nanosheets with an ultrahigh yield (~93%) and preserved surface chemistry. The resultant MXenes dispersions were employed as lubricants to enhance tribovoltaic nanogenerators, where Ti3C2Br2 displayed superior electrical output. These findings facilitate the understanding of MXenes’ intrinsic physical properties and enable the nanoengineering of advanced electronic devices. Copyright © 2024, The Authors. All rights reserved.

关键词： Nanosheets

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Disentangling high-order mechanisms and high-order behaviours in complex systems

arXiv

引用

arXiv 2022年

作者： Rosas, Fernando E. Mediano, Pedro A.M. Luppi, Andrea I. Varley, Thomas F. Lizier, Joseph T. Stramaglia, Sebastiano Jensen, Henrik J. Marinazzo, Daniele Data Science Institute Imperial College London United Kingdom Centre for Psychedelic Research Imperial College London United Kingdom Centre for Complexity Science Imperial College London United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Psychology Queen Mary University of London United Kingdom University Division of Anaesthesia University of Cambridge United Kingdom Department of Clinical Neurosciences University of Cambridge United Kingdom Leverhulme Centre for the Future of Intelligence University of Cambridge United Kingdom Department of Psychological & Brain Sciences Indiana University BloomingtonIN United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Centre for Complex Systems Faculty of Engineering The University of Sydney Australia Dipartimento Interateneo di Fisica Università Degli Studi di Bari Aldo Moro Italy INFN Sezione di Bari Italy Department of Mathematics Imperial College London United Kingdom Institute of Innovative Research Tokyo Institute of Technology Japan Department of Data Analysis Ghent University Belgium

Battiston et al. (arXiv:2110.06023) provide a comprehensive overview of how investigations of complex systems should take into account interactions between more than two elements, which can be modelled by hypergraphs and studied via topological data analysis. Following a separate line of enquiry, a broad literature has developed information-theoretic tools to characterize high-order interdependencies from observed data. While these could seem to be competing approaches aiming to address the same question, in this correspondence we clarify that this is not the case, and that a complete account of higher-order phenomena needs to embrace both. Copyright © 2022, The Authors. All rights reserved.

关键词： Large scale systems

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基于大语言模型的糖尿病管理:潜力与展望

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science Bulletin 2024年第5期69卷 583-588页

作者：盛斌管洲榆 Lee-Ling Lim 江泽铧 Nestoras Mathioudakis 李佳佳刘茹涵包玉倩 Yong Mong Bee 王亚星郑颖丰 Gavin Siew Wei Tan 纪宏伟 Josip Car 王海波 David C.Klonoff 李华婷覃宇宗黄天荫贾伟平 Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MOE Key Laboratory of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Medicine Faculty of MedicineUniversity of MalayaKuala Lumpur 50603Malaysia Department of Medicine and Therapeutics The Chinese University of Hong KongHong KongChina Asia Diabetes Foundation Hong KongChina Tsinghua Medicine of Tsinghua University Beijing 100084China School of Clinical Medicine Beijing Tsinghua Changgung HospitalBeijing 102218China Division of Endocrinology Diabetes&MetabolismJohns Hopkins University School of MedicineBaltimoreMD 21211USA Department of Endocrinology Singapore General HospitalSingapore 169608Singapore Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital University of Medical ScienceBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhou 510060China Singapore Eye Research Institute Singapore National Eye CentreSingapore 168751Singapore Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China Centre for Population Health Sciences Lee Kong Chian School of MedicineNanyang Technological University SingaporeSingapore 639798Singapore Department of Primary Care and Public Health School of Public HealthImperial College LondonLondon SW72BUUK Clinical Trial Unit Research Centre of Big Data and Artificial Intelligence in Medicine

The increasing prevalence of diabetes has become a global public health concern in the 21st *** 2021,it was estimated that 537 million people had diabetes,and this number is projected to reach 643 million by 2030,and 783 million by 2045[1].Such a huge burden of diabetes brings great challenges in its prevention and management,including early diagnosis,timely interventions,and regular monitoring of risk factor control and complications *** self-care support and patient empowerment can enhance clinical and psychobehavioural outcomes[2],although these require additional resources including manpower,infrastructure(hard and technology),and *** emergence of digital health technologies(DHTs),especially artificial intelligence(AI),may help address these obstacles and alleviate the burden of diabetes[3].Large language models(LLMs),a generative AI that can accept image and text inputs and produce text outputs,have shown promise in various aspects of medical care.

关键词： prevention diagnosis finance

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Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach

arXiv

引用

arXiv 2021年

作者： Chen, Wei-Kun Liu, Ya-Feng Liu, Fan Dai, Yu-Hong Luo, Zhi-Quan School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

In this paper, we propose an efficient algorithm for the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements. The problem has been formulated as a mixed integer linear programming (MILP) formulation in the literature. We first propose a novel linear programming (LP) relaxation of the MILP formulation. We show that compared with a natural LP relaxation of the MILP formulation, the novel LP relaxation is much more compact in terms of smaller numbers of variables and constraints, and much stronger in terms of providing a better LP bound, which makes it particularly suitable to be embedded in an LP relaxation based algorithm. Then we design an efficient two-stage LP dynamic rounding-and-refinement algorithm based on this novel LP relaxation. In the first stage, the proposed algorithm uses an LP dynamic rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration;in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between the solution quality and the computational complexity. In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

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Unsupervised deep learning for instrumented infrastructure: A case study 2

Unsupervised deep learning for instrumented infrastructure: ...

引用

2nd International Conference on Smart Infrastructure and Construction: Driving data-Informed Decision-Making, ICSIC 2019

作者： Mikhailova, A. Adams, N.M. Hallsworth, C.A. Lau, F.D.-H. Jones, D.N. Department of Mathematics Imperial College of Science Technology and Medicine London United Kingdom Data Science Institute Imperial College of Science Technology and Medicine London United Kingdom Lloyd's Register Foundation Programme on Data-Centric Engineering Alan Turing Institute London United Kingdom Mathematical Institute University of Oxford Oxford United Kingdom

ISBN: (纸本)9780727764669

Deep learning methods have recently shown great success in numerous fields, including finance, healthcare, linguistics, robotics, and even cybersports. Modern computer hardware makes it possible to train very large deep neural networks that can handle datasets of high dimensionality and rich structure. The area of unsupervised deep learning addresses modelling underlying data distributions when no ground truth labels are present. Probabilistic deep learning studies models that provide a measure of uncertainty for a prediction. We present a case study where a state-of-the-art family of deep probabilistic unsupervised models called Variational Auto-Encoder (VAE) is applied to data accrued from a network of fibre-optic sensors installed within a composite steel-concrete half-through railway bridge. Our goals were to: 1) automatically characterise the response of the bridge under stimuli based on sensor measurements, and 2) based on this characterisation, determine when a train passes across a bridge. Based on the VAE model, we present an algorithm to automatically identify the "train event" points. Our case study illustrates how state-of-the-art deep learning methods can be applied to a civil infrastructure engineering problem without directly modelling the physics of the objects or using any labels. © International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving data-Informed Decision-Making.

关键词： Computer hardware

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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

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Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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Observation of gamma rays up to 320 TeV from the middle-aged TeV pulsar wind nebula HESS J1849-000

arXiv

引用

arXiv 2023年

作者： Amenomori, M. Asano, S. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gomi, A. Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Kurashige, D. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Lu, H. Meng, X.R. Meng, Y. Munakata, K. Nagaya, K. Nakamura, Y. Nakazawa, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakakibara, Y. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Wang, H. Wang, S.F. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, Y.L. Yuan, A.F. Zhai, L.M. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Department of Physics Hirosaki University Hirosaki036-8561 Japan Department of Physics Shinshu University Matsumoto390-8621 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China College of Industrial Technology Nihon University Narashino275-8575 Japan National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo184-8795 Japan Department of Mechanical and Electrical Engineering Shangdong Management University Jinan250357 China College of Science China University of Petroleum Qingdao266555 China Tok

Gamma rays from HESS J1849−000, a middle-aged TeV pulsar wind nebula (PWN), are observed by the Tibet air shower array and the muon detector array. The detection significance of gamma rays reaches 4.0 σ and 4.4 σ levels above 25 TeV and 100 TeV, respectively, in units of Gaussian standard deviation σ. The energy spectrum measured between 40 TeV −16(E/40 TeV)−2.24±0.41 TeV−1 cm−2 s−1. The gamma-ray energy spectrum from the sub-TeV (E p, cut is estimated at (Equation presented), suggesting that protons are accelerated up to the PeV energy range. Our study thus proposes that HESS J1849−000 should be further investigated as a new candidate for a Galactic PeV cosmic-ray accelerator, PeVatron. © 2023, CC BY.

关键词： Gamma rays

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